{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "import pandas as pd\n",
    "\n",
    "# 获取数据\n",
    "rrr_data = ts.get_rrr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据存储为excel文件\n",
    "rrr_data.to_excel(r'../dataFiles/rrr.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>before</th>\n",
       "      <th>now</th>\n",
       "      <th>changed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-25</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>14.00</td>\n",
       "      <td>13.5</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-15</td>\n",
       "      <td>15.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>15.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-25</td>\n",
       "      <td>16.50</td>\n",
       "      <td>15.5</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>17.00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-10-24</td>\n",
       "      <td>17.50</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-09-06</td>\n",
       "      <td>18.00</td>\n",
       "      <td>17.5</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>18.50</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>19.50</td>\n",
       "      <td>18.5</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>20.00</td>\n",
       "      <td>19.5</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012-05-18</td>\n",
       "      <td>20.50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-02-24</td>\n",
       "      <td>21.00</td>\n",
       "      <td>20.5</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>21.50</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011-06-20</td>\n",
       "      <td>21.00</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2011-05-18</td>\n",
       "      <td>20.50</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2011-04-21</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2011-03-25</td>\n",
       "      <td>19.50</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011-02-24</td>\n",
       "      <td>19.00</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2011-01-20</td>\n",
       "      <td>18.50</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2010-12-20</td>\n",
       "      <td>18.00</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2010-11-29</td>\n",
       "      <td>17.50</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>17.00</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2010-05-10</td>\n",
       "      <td>16.50</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2010-02-25</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2010-01-18</td>\n",
       "      <td>15.50</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2008-12-25</td>\n",
       "      <td>16.00</td>\n",
       "      <td>15.5</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>17.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2008-10-15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2008-09-25</td>\n",
       "      <td>17.50</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2008-06-25</td>\n",
       "      <td>17.00</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2008-06-15</td>\n",
       "      <td>16.50</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2008-05-20</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2008-04-25</td>\n",
       "      <td>15.50</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2008-03-25</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2008-01-25</td>\n",
       "      <td>14.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2007-12-25</td>\n",
       "      <td>13.50</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2007-11-26</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2007-10-25</td>\n",
       "      <td>12.50</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2007-09-25</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2007-08-15</td>\n",
       "      <td>11.50</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2007-06-05</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2007-05-15</td>\n",
       "      <td>10.50</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2007-04-16</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2007-02-25</td>\n",
       "      <td>9.50</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2007-01-15</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2006-11-15</td>\n",
       "      <td>8.50</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2006-08-15</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2006-07-05</td>\n",
       "      <td>7.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2004-04-25</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2003-09-21</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1999-11-21</td>\n",
       "      <td>8.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1998-03-21</td>\n",
       "      <td>13.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1988-09-30</td>\n",
       "      <td>12.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1987-12-31</td>\n",
       "      <td>10.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1985-12-31</td>\n",
       "      <td>--</td>\n",
       "      <td>10.0</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date before   now changed\n",
       "0   2019-01-25  13.50  13.0   -0.50\n",
       "1   2019-01-15  14.00  13.5   -0.50\n",
       "2   2018-10-15  15.00  14.0   -1.00\n",
       "3   2018-07-05  15.50  15.0   -0.50\n",
       "4   2018-04-25  16.50  15.5   -1.00\n",
       "5   2016-03-01  17.00  16.5   -0.50\n",
       "6   2015-10-24  17.50  17.0   -0.50\n",
       "7   2015-09-06  18.00  17.5   -0.50\n",
       "8   2015-06-28  18.50  18.0   -0.50\n",
       "9   2015-04-20  19.50  18.5   -1.00\n",
       "10  2015-02-05  20.00  19.5   -0.50\n",
       "11  2012-05-18  20.50  20.0   -0.50\n",
       "12  2012-02-24  21.00  20.5   -0.50\n",
       "13  2011-12-05  21.50  21.0   -0.50\n",
       "14  2011-06-20  21.00  21.5    0.50\n",
       "15  2011-05-18  20.50  21.0    0.50\n",
       "16  2011-04-21  20.00  20.5    0.50\n",
       "17  2011-03-25  19.50  20.0    0.50\n",
       "18  2011-02-24  19.00  19.5    0.50\n",
       "19  2011-01-20  18.50  19.0    0.50\n",
       "20  2010-12-20  18.00  18.5    0.50\n",
       "21  2010-11-29  17.50  18.0    0.50\n",
       "22  2010-11-16  17.00  17.5    0.50\n",
       "23  2010-05-10  16.50  17.0    0.50\n",
       "24  2010-02-25  16.00  16.5    0.50\n",
       "25  2010-01-18  15.50  16.0    0.50\n",
       "26  2008-12-25  16.00  15.5   -0.50\n",
       "27  2008-12-05  17.00  16.0   -1.00\n",
       "28  2008-10-15  17.50  17.0   -0.50\n",
       "29  2008-09-25  17.50  17.5    0.00\n",
       "30  2008-06-25  17.00  17.5    0.50\n",
       "31  2008-06-15  16.50  17.0    0.50\n",
       "32  2008-05-20  16.00  16.5    0.50\n",
       "33  2008-04-25  15.50  16.0    0.50\n",
       "34  2008-03-25  15.00  15.5    0.50\n",
       "35  2008-01-25  14.50  15.0    0.50\n",
       "36  2007-12-25  13.50  14.5    1.00\n",
       "37  2007-11-26  13.00  13.5    0.50\n",
       "38  2007-10-25  12.50  13.0    0.50\n",
       "39  2007-09-25  12.00  12.5    0.50\n",
       "40  2007-08-15  11.50  12.0    0.50\n",
       "41  2007-06-05  11.00  11.5    0.50\n",
       "42  2007-05-15  10.50  11.0    0.50\n",
       "43  2007-04-16  10.00  10.5    0.50\n",
       "44  2007-02-25   9.50  10.0    0.50\n",
       "45  2007-01-15   9.00   9.5    0.50\n",
       "46  2006-11-15   8.50   9.0    0.50\n",
       "47  2006-08-15   8.00   8.5    0.50\n",
       "48  2006-07-05   7.50   8.0    0.50\n",
       "49  2004-04-25   7.00   7.5    0.50\n",
       "50  2003-09-21   6.00   7.0    1.00\n",
       "51  1999-11-21   8.00   6.0   -2.00\n",
       "52  1998-03-21  13.00   8.0   -5.00\n",
       "53  1988-09-30  12.00  13.0    1.00\n",
       "54  1987-12-31  10.00  12.0    2.00\n",
       "55  1985-12-31     --  10.0      --"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rrr_data = pd.read_excel(r'../dataFiles/rrr.xlsx',encoding='ansi')\n",
    "rrr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date       2019-01-25\n",
       "before           9.50\n",
       "now              21.5\n",
       "changed          2.00\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rrr_data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对指定列求最大值\n",
    "rrr_data['now'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "需求：  \n",
    "将存款准备金率的变动绘制成折线图；  \n",
    "横轴：年份；纵轴：存款准备金率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xba47ba8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x444.96 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams[\"font.sans-serif\"] = \"SimHei\"\n",
    "\n",
    "# 创建一个画布\n",
    "fig = plt.figure(figsize=(10,6.18))\n",
    "\n",
    "# 建立一个坐标系\n",
    "plt.subplot(111)\n",
    "\n",
    "x = rrr_data['date'].iloc[0:9]\n",
    "y = rrr_data['now'].iloc[0:9]\n",
    "\n",
    "# 绘图\n",
    "plt.plot(x,y,color='k',linestyle='dashdot',linewidth=1,label='存款准备金率')\n",
    "plt.title(\"存款准备金率变动情况\", loc='center',fontsize='xx-large')\n",
    "\n",
    "# 添加数据标签\n",
    "for a,b in zip(x,y):\n",
    "    plt.text(a,b,b,ha='center',va='bottom',fontsize=10)\n",
    "    \n",
    "# xx-small | x-small | small | medium | large | x-large | xx-large\n",
    "plt.xlabel('时间',fontsize='x-large')\n",
    "plt.ylabel(\"存款准备金率\",labelpad=10,fontsize='x-large')\n",
    "\n",
    "# 设置图例，调用显示出plot中的label值\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
